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Using wireless signals to monitor human vital signs, especially heartbeat information, has been intensively studied in the past decade. This non-contact sensing modality can drive various applications from cardiac health, sleep, and emotion management. Under the circumstance of the COVID-19 pandemic, non-contact heart monitoring receives increasingly market demands. However, existing wireless heart monitoring schemes can only detect limited heart activities, such as heart rate, fiducial points, and Seismocardiography (SCG)-like information. In this paper, we present CardiacWave to enable a non-contact high-definition heart monitoring. CardiacWave can provide a full spectrum of Electrocardiogram (ECG)-like heart activities, including the details of P-wave, T-wave, and QRS complex. Specifically, CardiacWave is built upon the Cardiac-mmWave scattering effect (CaSE), which is a variable frequency response of the cardiac electromagnetic field under the mmWave interrogation. The CardiacWave design consists of a noise-resistant sensing scheme to interrogate CaSE and a cardiac activity profiling module for extracting cardiac electrical activities from the interrogation response. Our experiments show that the CardiacWave-induced ECG measures have a high positive correlation with the heart activity ground truth (i.e., measurements from a medical-grade instrument). The timing difference of P-waves, T-waves, and QRS complex is 0.67%, 0.71%, and 0.49%, respectively, and a mean cardiac event difference is within a delay of 5.3 milliseconds. These results indicate that CaridacWave offers high-fidelity and integral heart clinical characteristics. Furthermore, we evaluate the CardiacWave system with participants under various conditions, including heart and breath rates, ages, and heart habits (e.g., tobacco use).

Early detection and accurate burden estimation of atrial fibrillation (AFib) can provide the foundation for effective physician treatment. New approaches to accomplish this have attracted tremendous attention in recent years. In this paper, we develop a novel passive smartwatch-based system to detect AFib episodes and estimate the AFib burden in an ambulatory free-living environment without user engagement. Our system leverages a built-in PPG sensor to collect heart rhythm without user engagement. Then, a data preprocessor module includes time-frequency (TF) analysis to augment features in both the time and frequency domain. Finally, a lightweight multi-view convolutional neural network consisting of 19 layers achieves the AFib detection. To validate our system, we carry out a research study that enrolls 53 participants across three months, where we collect and annotate more than 27,622 hours of data. Our system achieves an average of 91.6% accuracy, 93.0% specificity, and 90.8% sensitivity without dropping any data. Moreover, our system takes 0.51 million parameters and costs 5.18 ms per inference. These results reveal that our proposed system can provide a clinical assessment of AFib in daily living.

Measurement of ambient fine particulate matter (PM2.5) is often used as a proxy of personal exposure in epidemiological studies. However, the difference between personal and ambient exposure, and whether it biases the estimates of health effects remain unknown. Based on an epidemiological study (AIRLESS) and simultaneously launched intensive monitoring campaigns (APHH), we quantified and compared the personal and ambient exposure to PM2.5 and the related health impact among residents in Beijing, China. In total, 123 urban and 128 peri-urban non-smoking participants were recruited from two well-established cohorts in Beijing. During winter 2016 and summer 2017, each participant was instructed to carry a validated personal air monitor (PAM) to measure PM2.5 concentration at high spatiotemporal resolution for seven consecutive days in each season. Multiple inflammatory biomarkers were measured, including exhaled NO, blood monocytes counts and C-reactive protein. Linear mixed-effect models were used for the associations between exposure and health outcomes with adjustment for confounders. The average level of daily personal exposure to PM2.5 was consistently lower than using corresponding ambient concentration, and the difference is greater during the winter. The personal to ambient (P/A) ratio of exposure to PM2.5 exhibited an exponentially declining trend, and showed larger variations when ambient PM2.5 levels < 25 μg m−3. Personal exposure to PM2.5 was significantly associated with the increase in respiratory and systemic inflammatory biomarkers; however, the associations were weaker or became insignificant when ambient concentrations were used. Exposure to ambient PM2.5 might not be a good proxy to estimate the health effect of exposure to personal PM2.5.

Parkinson's disease (PD) can gradually affect people's lives thus attracting tremendous attention. Early PD detection and treatment can help control the disease progress, relief from the symptoms and improve the patients' life quality. However, the current practice of PD diagnosis is conducted in a clinical setup and administrated by a PD specialist due to the early signs of PD are not noticeable in daily life. According to the report of CDC/NIH, the diagnosed time of PD ranges from 2-10 years after onset. Therefore, a more accessible PD diagnosis approach is urgently demanded. In recent years, mobile health (for short mHealth) technology has been intensively investigated for preventive medicine, particularly in chronic disease management. Notably, many types of research have explored the possibility of using mobile and wearable personal devices to detect the symptom of PD and shown promising results. It provides opportunities for transforming early PD detection from clinical to daily life. This survey paper attempts to conduct a comprehensive review of mHealth technologies for PD detection from 2000 to 2019, and compares their pros and cons in practical applications and provides insights to close the performance gap between state-of-the-art clinical approaches and mHealth technologies.

Hanbin Zhang,1 Wenrui Lin,2 Dongting Chen,1 Kenie Wang,3 Wei Tu,4 Huiwen Lin,4 Kun Li,4 Shangzhu Ye,5 Tianwang Guan,6 Yuerong Chen4 1Department of Clinical Medicine, The Second Clinical College of Guangzhou Medical University, Guangzhou, People’s Republic of China; 2Department of Clinical Medicine, The Sixth Clinical College of Guangzhou Medical University, Guangzhou, People’s Republic of China; 3The First Department of Breast Cancer, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin, People’s Republic of China; 4Department of Oncology, Jinshazhou Hospital of Guangzhou University of Chinese Medicine, Guangzhou, People’s Republic of China; 5Department of Ophthalmology and Otorhinolaryngology, Yangjiang Hospital of Traditional Chinese Medicine, Yangjiang, People’s Republic of China; 6Department of Cardiology, Laboratory of Heart Center, Zhujiang Hospital, Southern Medical University, Guangzhou, People’s Republic of ChinaCorrespondence: Tianwang GuanDepartment of Cardiology, Laboratory of Heart Center, Zhujiang Hospital, Southern Medical University, Guangzhou, People’s Republic of ChinaTel +8620-61643888Email guantianwang@163.comYuerong ChenDepartment of Oncology, Jinshazhou Hospital of Guangzhou University of Chinese Medicine, Guangzhou, People’s Republic of ChinaTel +8620-81116661Email 604910344@qq.comPurpose: Male breast cancer (MBC) is a rare disease that tends to occur in elderly men. Little is known about the causes of death in MBC because of the small sample size of most studies. This study aimed to investigate the causes of death in MBC patients.Patients and Methods: MBC patient data were obtained from the Surveillance, Epidemiology, and End Results database (1975– 2016). Time trends of MBC mortality in the US population were analyzed using Joinpoint software. We calculated the proportion of each cause of death in the overall cohort and in different patient subgroups. Competing risk models were used to calculate cumulative mortality at different follow-up times. The risk of cardiovascular death (CVD) in MBC patients was compared to that of the age-matched general population by calculating standardized mortality ratio (SMR).Results: In total, 6426 patients were included in the analysis. MBC mortality rate increased between 2004 and 2019 (annual percentage change=1.16, 95% confidence interval [CI]: 0.50, 1.80). There were 1757 patients (27.3%) who died of non-breast cancer causes. CVD was the leading cause of death in patients who were elderly or had localized disease. MBC patients had a 6.58-fold higher risk of CVD than the general population (SMR=6.58, 95% CI: 6.14, 7.05).Conclusion: Non-breast cancer death accounts for the majority of deaths in MBC patients who are elderly or have localized cancer. Compared to the general population, MBC patients have an increased risk of CVD. These results highlight the importance of monitoring cardiovascular comorbidities in MBC patients.Keywords: male breast cancer, cardio-oncology, elderly patients, cause of death, non cancer death, cardiovascular death

The medicine adherence in Parkinson's disease (PD) treatment has attracted tremendous attention due to the critical consequences it can lead to otherwise. As a result, clinics need to ensure that the medicine intake is performed on time. Existing approaches, such as self-report, family reminder, and pill counts, heavily rely on the patients themselves to log the medicine intake (hereafter, patient involvement). Unfortunately, PD patients usually suffer from impaired cognition or memory loss, which leads to the so-called medication non-adherence, including missed doses, extra doses, and mistimed doses. These instances can nullify the treatment or even harm the patients. In this paper, we present PDMove, a smartphone-based passive sensing system to facilitate medication adherence monitoring without the need for patient involvement. Specifically, PDMove builds on the fact that PD patients will present gait abnormality if they do not follow medication treatment. To begin with, PDMove passively collects gait data while putting the smartphone in the pocket. Afterward, the gait preprocessor helps extract gait cycle containing the Parkinsonism-related biomarkers. Finally, the medicine intake detector consisting of a multi-view convolutional neural network predicts the medicine intake. In this way, PDMove enables the medication adherence monitoring. To evaluate PDMove, we enroll 247 participants with PD and collect more than 100,000 gait cycle samples. Our results show that smartphone-based gait assessment is a feasible approach to the AI-care strategy to monitor the medication adherence of PD patients.

BackgroundCardiovascular death (CVD) in breast cancer patients without chemotherapy (CT) or (and) radiotherapy (RT) has not been studied yet. This study evaluates the correlation between breast cancer and CVD risk independent of chemotherapy or (and) radiotherapy.MethodsData of female breast cancer patients without receiving CT or RT were retrieved from the Surveillance, Epidemiology, and End Result (SEER) database (2004–2015). Data were divided into two cohorts: tumor resection cohort and no resection cohort. The CVD risk in patients was expressed as standardized mortality ratios (SMRs). A 1:1 propensity score matching (PSM) was applied to balance inter-group bias, and competing risk regressions were utilized to evaluate the impact of tumor resection on CVD.ResultsThe CVD risk was significantly higher (SMR = 2.196, 95% CI: 2.148–2.245, PPPP=0.009).ConclusionsFemale breast cancer patients are at higher risk of CVD despite unexposure to cardio-toxic CT or RT. However, female breast cancer patients subjected to tumor resection have decreased CVD risk. These results indicated that monitoring female breast cancer patients not receiving RT or CT might serve as a preventative measure against CVD.

Background: Premature ovarian insufficiency (POI) is characterized by a loss of ovarian function before 40 years-of-age and represents an existing challenge cause of female infertility. POI is one of the dominant causes of cis-diaminedichloroplatinum (cisplatin, CDDP)-induced reproductive impairment. However, the detailed mechanisms underlying POI induced by CDDP remain unclear.Methods: The POI C57B6/J mouse model was created by administering CDDP. The effects of FKBP4 were investigated using isobaric tags for relative and absolute quantification analysis (iTRAQ), real-time quantitative PCR (qRT-PCR) and western blotting. Target prediction was predicted using TargetScan software. Levels of sex hormones were tested using Enzyme-linked immunosorbent assays (ELISA).Results: We found that the FKBP4 protein was down-regulated in the ovaries of CDDP model. Target prediction identified FKBP4 as a potential target for miR-483-5p, which was expressed at high levels in both the ovaries and serum of CDDP-POI mice, and in the serum from POI patients. In vitro experiments further confirmed that FKBP4 was the target for miR-483-5p in human cervical cancer cells (HeLa). The overexpression of FKBP4 in human granulosa cells (KGN) alleviated the apoptosis caused by CDDP and the overexpression of miR-483-5p. Furthermore, the overexpression of miR-483-5p in oocytes caused injury to the ovaries, and disrupted the levels of sex hormones in CDDP-POI mice (AMH: P < 0.01; E2: P < 0.01; FSH: P < 0.01).Conclusions: Analyses showed that miR-483-5p targets the FKBP4 protein in a mouse model of POI induced by CDDP. Elevated levels of miR-483-5p in oocytes could aggravate POI induced by CDDP by targeting FKBP4. Overall, our data demonstrate that miR-483-5p was responsible for the underlying pathophysiology of POI induced by chemotherapeutic treatments, such as CDDP.

Approximately 7 million survivors of stroke reside in the United States. Over half of these individuals will have residual deficits, making stroke one of the leading causes of disability. Long-term rehabilitation opportunities are critical for millions of individuals with chronic upper limb motor deicits due to stroke. Traditional in-home rehabilitation is reported to be dull, boring, and un-engaging. Moreover, existing rehabilitation technologies are not user-friendly and cannot be adaptable to different and ever-changing demands from individual stroke survivors. In this work, we present RehabPhone, a highly-usable software-defined stroke rehabilitation paradigm using the smartphone and 3D printing technologies. This software-definition has twofold. First, RehabPhone leverages the cost-effective 3D printing technology to augment ordinal smartphones into customized rehabilitation tools. The size, weight, and shape of rehabilitation tools are software-defined according to individual rehabilitation needs and goals. Second, RehabPhone integrates 13 functional rehabilitation activities co-designed with stroke professionals into a smartphone APP. The software utilizes built-in smartphone sensors to analyzes rehabilitation activities and provides real-time feedback to coach and engage stroke users. We perform the in-lab usability optimization with the RehabPhone prototype with involving 16 healthy adults and 4 stroke survivors. After that, we conduct a 6-week unattended intervention study in 12 homes of stroke residence. In the course of the clinical study, over 32,000 samples of physical rehabilitation activities are collected and evaluated. Results indicate that stroke users with RehabPhone demonstrate a high adherence and clinical efficacy in a self-managed home-based rehabilitation course. To the best of our knowledge, this is the first exploratory clinical study using mobile health technologies in real-world stroke rehabilitation.

Background The objectives of this study were to screen out cut-off age value and age-related differentially expressed genes (DEGs) in clear cell renal cell carcinoma (CCRCC) from Surveillance Epidemiology and End Results (SEER) database and The Cancer Genome Atlas (TCGA) database. Methods We selected 45,974 CCRCC patients from SEER and 530 RNA-seq data from TCGA database. The age cut-off value was defined using the X-tile program. Propensity score matching (PSM) was used to balance the differences between young and old groups. Hazard ratio (HR) was applied to evaluate prognostic risk of age in different subgroups. Age-related DEGs were identified via RNA-seq data. Survival analysis was used to assess the relationship between DEGs and prognosis. Results In this study, we divided the patients into young (n = 14,276) and old (n = 31,698) subgroups according to cut-off value (age = 53). Age > 53 years was indicated as independent risk factor for overall survival (OS) and cancer specific survival (CSS) of CCRCC before and after PSM. The prognosis of old group was worse than that in young group. Eleven gene were differential expression between the younger and older groups in CCRCC. The expression levels of PLA2G2A and SIX2 were related to prognosis of the elderly. Conclusion Fifty-three years old was cut-off value in CCRCC. The prognosis of the elderly was worse than young people. It remind clinicians that more attention and better treatment should be given to CCRCC patients who are over 53 years old. PLA2G2A and SIX2 were age-related differential genes which might play an important role in the poor prognosis of elderly CCRCC patients.

Objective. This study is aimed at constructing and verifying nomograms that forecast overall survival (OS) and cancer-specific survival (CSS) of children with Wilms’ tumor (WT). Patients and methods. Clinical information of 1613 WT patients who were under 18 years old between 1988 and 2010 was collected from the Surveillance, Epidemiology, and End Results (SEER) database. Using these data, we performed univariate as well as multivariate Cox’s regression analyses to determine independent prognostic factors for WT. Then, nomograms to predict 3- and 5-year OS and CSS rates were constructed based on the identified prognostic factors. The nomograms were validated externally and internally. The nomograms’ reliability was evaluated utilizing receiver operating characteristic (ROC) curves and concordance indices (C-indices). Results. 1613 WT patients under 18 were involved in the study and randomly divided into the training (n=1210) and validation (n=403) cohorts. Age at diagnosis, tumor laterality, tumor size, tumor stage, and use of surgery were determined as independent prognostic factors for OS and CSS in WT and were further applied to construct prognostic nomograms. The C-index and area under the receiver operating characteristic curve (AUC) revealed the great performance of our nomograms. Internal and external calibration plots also showed excellent agreement between actual survival and nomogram prediction. Conclusion. Precise and convenient nomograms were developed for forecasting OS and CSS of children with WT. These nomograms were able to offer accurate and individualized prognosis and assisted clinicians in performing suitable therapy.

The energy internet at the park level is one of the most suitable locations for carrying out the integrated energy service at the background of low carbon economy, which can significantly improve the energy structure of China. In this paper, the concept of Park Energy Internet (PEI)was introduced. The main characteristics and functions of PEI was analyzed in detail. The developments of PEI around the world such as construction objective and application results, were introduced. After analysis, the insufficient of traditional PEI planning, key technical issue of PEI planning was explored in this paper. Furthermore, framework and critical factors of PEI planning method were proposed. It is pointed out that engineering practice should develop from optimization planning, information platform, and energy interaction, and the development direction and principle are put forward as well, improving the developing pattern of PEI optimal planning.

Taking Wumeng mountainous area in Guizhou province as the study area, by collecting landslide data and on-the-spot investigation records, a spatial database of 939 landslides and related influencing factors on GIS platform are established. The influence of altitude, relative height difference, slope angle, slope aspect, the shortest distance from landslide to rivers is studied. The results show that the number of landslides decreases exponentially with the shortest distance to the rivers. The most important factor affecting landslides distribution is topography and morphological, especially slope. The research can help human beings get along better with the surrounding nature environment and provide a basis and reference for environmental governance, landslide prevention and mitigation work.

Mechanistic target of rapamycin complex 1 (mTORC1) signaling is active in inflammation, but its involvement in septic acute kidney injury (AKI) has not been shown. mTORC1 activation (p-S6) in renal fibroblasts was increased in a mouse AKI model induced by 1.5 mg/kg lipopolysaccharide (LPS). Deletion of tuberous sclerosis complex 1 (TSC1), an mTORC1 negative regulator, in fibroblasts (Fibro-TSC1−/−) inhibited the elevation of serum creatinine and blood urea nitrogen in AKI compared with that in TSC1fl/fl control mice. Endothelin-1 (EDN1) and phospho-Jun-amino-terminal kinase (p-JNK) were up-regulated in Fibro-TSC1−/− renal fibroblasts after LPS challenge. Rapamycin, an mTORC1 inhibitor, and bosentan, an EDN1 antagonist, eliminated the difference in renal function between TSC1fl/fl and Fibro-TSC1−/− mice after LPS injection. Rapamycin restored LPS-induced up-regulation of EDN1, endothelin converting enzyme-1 (ECE1), and p-JNK in TSC1-knockdown mouse embryonic fibroblasts (MEFs). SP600125, a Jun-amino-terminal kinase (JNK) inhibitor, attenuated LPS-induced enhancement of EDN1 and ECE1 in TSC1-knockdown MEFs without a change in phospho-S6 ribosomal protein (p-S6) level. The results indicate that mTORC1–JNK-dependent up-regulation of ECE1 elevated EDN1 in TSC1-knockout renal fibroblasts and contributed to improvement of renal function in Fibro-TSC1−/− mice with LPS-induced AKI. Renal fibroblast mTORC1 plays an important role in septic AKI.

The millimeter-wave (mm-Wave) or 60 GHz technology emerges as an attractive candidate for indoor wireless access in the 5G architecture. Different from 2.4/5 GHz, high signal attenuation requires mm-Wave antenna utilizing directional transmission to enhance beamforming gain. Consequently, time-consuming beamforming training process between mm-Wave nodes significantly increases communication overhead, especially when the environment is crowded since the nodes perform training process in a contention and backoff manner. In this paper, we propose a novel group beam training scheme that enables simultaneous beam training of all the user devices attempting to associate with the access point. Leveraging the angle-of-arrival sparsity in mm-Wave communications, compressed sensing is adopted to further reduce the beam training overhead. To verify the feasibility of group training and the necessity of compressed sensing under certain conditions, we analyze the signal-to-noise ratio measured on a 60 GHz software defined radio testbed in three typical indoor environments: i) corridor; ii) conference room; and iii) laboratory. Extensive simulations are also performed to evaluate the recovery performance of compressed sensing in mm-Wave WLANs for different sampling capabilities. Simulation results show that compressed sensing reduces the cost of sector sweep by 50%.

Parkinsons disease (PD) identification has at­tracted a lot of attention in recent years. However, there is still no standardized and convenient way to identify PD, because most researchers are only focusing on promoting identification accuracy. With the recent development of mobile health, a feasible mobile application to achieve PD identification is highly demanded with a small amount of information but can provide reliable results. To this end, we propose DeepVoice, a voiceprint-based PD identification application simultaneously integrating deep learning and mobile health. DeepVoice works by collecting a short period of monosyllabic voice through a mobile health App on a smartphone. Specifically, we propose the Joint Time-Frequency Analysis algorithm to enhance the voiceprint feature in spectrogram domain. We also develop a customized convolutional neural network (CNN) to complete the final identification. We evaluate our proposed DeepVoice on input data format, the length of the input voice and neural network architecture in a large PD dataset. Experimental results show DeepVoice could successfully achieve PD identification with an accuracy of 90.45±1.71% with only 10 seconds long audio segment. Our study also reveals that the smartphone-based mobile health application is feasible for PD identification.

Remote sensing has the characteristics of multi-temporal, multi-semantic and multi-spectral, and it plays an important role in most kinds of fields, so we should take some measures to protect the security of the remote sensing. With the rapid development of technology in three-dimensional remote sensing, it has promoted the remote sensing data growth explosively, and it has shown the big data characteristics obviously. Because of the non-complete trusted cloud environment, we consider a security homomorphic encryption scheme over the most significant bit in cloud, which can support the operation for ciphertext remote image in cloud.

The aim of the study is to investigate the relationship between physical activity as assessed by accelerometers and cognitive development across the human age ranges (from children and adolescents to adults). Additionally, this study seeks to explore whether physical activity contributes to cognitive development via modification of plasma insulin-like growth factor 1 (IGF-1) and brain-derived neurotrophic factor (BDNF). In the study, 500 preschool children (3.5–5.5 years old) are taking part in 6 triennial assessment waves over the span of 15 years. At each wave, participant measures included (a) 7-day physical activity monitoring using ActiGraph’s GT3X accelerometers, (b) the evaluation of cognitive development, (c) anthropometric and physical fitness assessments, (d) plasma IGF-1 and BDNF concentrations, and (e) retrospective questionnaires. Linear regression models are used to examine the effect of physical activity on cognitive development; plasma IGF-1 and BDNF concentrations are considered as mediators into data analyses. The results of the study may help to inform future health interventions that utilize physical activity as a means to improve cognitive development in children, adolescents, and adults. Additionally, the study may assist in determining whether the putative effects occur via modification of plasma IGF-1 or BDNF concentrations.

Purpose. To evaluate the relationships between objectively measured physical activity and physical fitness among preschool children. Methods. A total of 346 participants (201 boys and 145 girls) aged 3.5–5.5 years (M=4.5 yr, SD = 0.47) from Shanghai, China, completed physical fitness assessments, including triceps skinfold thickness (TSFT), grip strength, tennis throwing, sit and reach test, standing long jump, balance beam, 10mSRT, and 20mSRT. Physical activity was objectively measured by ActiGraphGT3X+ accelerometer. Multiple linear regression models were used to explore the cross-sectional associations between PA and physical fitness after adjusting for age, gender, BMI, and valid wearing time. Results. Positive associations were observed between stand long jump (p<.01), tennis throwing (p<.01), laps in 20mSRT (p<.01), and MVPA. However, TSFT (p<.05), time in 10mSRT (p<.01), and balance beam (p<.05) were negatively associated with MVPA. Furthermore, positive associations were found between stand long jump (p<.01), tennis throwing (p<.01), and MVPA only in boys. Negative associations were found between time on balance beam (p<.01) and MVPA only in girls. Conclusions. MVPA appears to be an effective and reliable predictor of preschoolers’ physical fitness. Boys’ body composition, muscular strength, explosive strength, agility, aerobic fitness, girls’ agility, aerobic fitness, and balance could improve as MVPA increases.

Many studies have evaluated the association between cyclin D1 (CCND1) G870A polymorphism and cervical cancer susceptibility. However, these studies showed inconsistent results. The aim of this study was to derive a more precise estimation of this association. We searched PubMed and Embase for related studies that had been published in English, and ten case-control studies with a total of 2,864 cases and 3,898 controls were finally identified to be eligible studies in the meta-analysis. The association was assessed by summarizing the odds ratios (ORs) with the corresponding 95 % confidence intervals (CIs). Overall, there was no significant association between cyclin D1 (CCND1) G870A polymorphism and cervical cancer risk (for the allele model A vs. G: OR = 1.02, 95 % CI 0.88-1.19, p = 0.76; for the co-dominant model AA vs. GG: OR = 1.03, 95 % CI 0.75-1.41, p = 0.85; for the dominant model AA + GA vs. GG: OR = 1.00, 95 % CI 0.78-1.28, p = 0.99; for the recessive comparison AA vs. GA + GG: OR = 1.06, 95 % CI 0.85-1.32, p = 0.62). In subgroup analysis by ethnicity, no significant difference was found in both Asians and Caucasians. In summary, the present meta-analysis provides evidence that genotypes for the cyclin D1 (CCND1) G870A polymorphism may be not associated with genetic susceptibility of cervical cancer.

ObjectiveEngaging in physical activity (PA) plays an important role in promoting physical and mental health, but the PA data for Chinese preschool children are lacking. This study aims to objectively assess the PA levels of preschool children in Shanghai, China and to evaluate their PA levels relative to age-specific recommendations.Design, setting and participantsA cross-sectional study was conducted among preschool children in Shanghai, city of China. There were a total of 303 preschool children (boys, 174; girls, 129) recruited from eight kindergarten classes in the Yangpu and Baoshan Districts of Shanghai.Main outcome measuresDaily PA was assessed using ActiGraph GT3X+accelerometers for seven consecutive days. Children were required to have data from at least 2 weekdays and 1 weekend day, with a minimum daily wear time of 480 min to be included in the analysis.ResultsPreschool children in Shanghai accumulated, on average, 70.9 min of moderate-to-vigorous PA (MVPA) and 168.0 min of total PA (TPA) per day (d). Boys engaged in more MVPA and TPA than girls (72.8 min/day vs 68.3 min/day and 171.9 min/day vs 162.9 min/day, respectively). Overall, 72.9% of the participants met the age-specific recommendations of MVPA, while 35.3% met TPA recommendations.ConclusionsFindings of this study warn of the insufficiency of PA in Shanghai preschool children, suggesting there is substantial room to improve their PA.Trial registration numberChiCTR-OOC-15007439; Results.

Early childhood is a critical period for development of cognitive function, but research on the association between physical activity and cognitive function in preschool children is limited and inconclusive. This study aimed to examine the association between technology-assessed physical activity and cognitive function in preschool children. A cross-sectional analysis of baseline data from the Physical Activity and Cognitive Development Study was conducted in Shanghai, China. Physical activity was measured with accelerometers for 7 consecutive days, and cognitive functions were assessed using the Chinese version of Wechsler Young Children Scale of Intelligence (C-WYCSI). Linear regression analyses were used to assess the association between physical activity and cognitive function. A total of 260 preschool children (boys, 144; girls, 116; mean age: 57.2 ± 5.4 months) were included in analyses for this study. After adjusting for confounding factors, we found that Verbal Intelligence Quotient, Performance Intelligence Quotient, and Full Intelligence Quotient were significantly correlated with light physical activity, not moderate to vigorous physical activity, in boys. Standardized coefficients were 0.211, 0.218, and 0.242 (all p < 0.05) in three different models, respectively. However, the correlation between physical activity and cognitive functions were not significant in girls (p > 0.05). These findings suggest that cognitive function is apparently associated with light physical activity in boys. Further studies are required to clarify the sex-specific effect on physical activity and cognitive functions.

Background/Aims: An increasing body of evidence demonstrates that L-carnitine plays a pivotal role in lipid metabolism of hemodialysis (HD) patients. However, there are still some reservations about its benefits. Therefore, we performed a meta-analysis to assess the effects of L-carnitine supplementation on lipid profile in HD patients. Methods: Literature search was performed to identify the relevant randomized controlled trials that investigated the effects of L-carnitine on the lipid profile of subjects. Two independent authors used an Excel file to extract data and assess trials quality. The primary effect measure was the difference in means of the final lipid measurements between the intervention and control groups. The meta-analysis was performed with the fixed-effects model or random-effects model according to heterogeneity. Results: Twelve studies with a total of 391 patients met the inclusion criteria. The use of L-carnitine was not associated with a reduction in the total cholesterol (SMD, -0.11; 95% CI, -0.31 to 0.09), HDL-cholesterol (SMD, 0.01; 95% CI, -0.36 to 0.39), VLDL-cholesterol (SMD, 0.54; 95% CI, -0.06 to 1.14), and the serum triglycerides (SMD, -0.12; 95% CI, -0.36 to 0.12). However, L-carnitine can significantly decrease the LDL-cholesterol (SMD, -0.29; 95% CI, -0.53 to -0.06) in HD patients. In a subgroup meta-analysis, a significant LDL-cholesterol-lowering effect of L-carnitine supplementation was observed in intravenous application group, and patients with longer interventional duration and renal diseases. Conclusion: The limited evidence suggests that there was no effect of L-carnitine on serum total cholesterol, HDL-cholesterol, VLDL-cholesterol and serum triglycerides. By contrast, this meta-analysis suggests a promising effect of L-carnitine on LDL-cholesterol. Further large-scale, well-designed randomized controlled trials are urgently needed

Background Little is known about the level of asthma control in Thai elementary school students living in the inner city of Bangkok. Our study aimed to evaluate the prevalence of asthma, level of asthma control and factors associated with asthma control in Thai students. Methods We conducted a cross-sectional descriptive study in students aged between 6-12 years at 3 public schools and 3 private schools in Bangkok. The parent-completed questionnaire used to assess the prevalence of asthma and asthma symptoms was translated from the Phase I ISAAC (The International Study of Asthma and Allergies in Childhood) questionnaire. Univariate analysis was used to identify possible risk factors related to partly and uncontrolled asthma. Results A population of 1,428 students was recruited by screening questionnaires (66.1% of response rate). The mean age was 9.5 years. Prevalence of physician-diagnosed asthma was 9% and the most common asthma symptom was night cough (23.8%). The level of asthma control was assessed in students with current asthma and classified as controlled (46.7%), partly controlled (43.3%) and uncontrolled (10%). Around 27% of students with current asthma in this study use controller medications. Factors associated with asthma control were analyzed but none approached significance. Conclusions The prevalence of asthma in elementary school students living in Bangkok has decreased since the previous survey and the use of controller medications has increased. Asthma management strategies should focus on encouraging awareness among physicians and parents about the importance of using controller medications to achieve better control of asthma.